Matching Items (987)
Description
Capturing the information in an image into a natural language sentence is
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
ContributorsKatpally, Harshitha (Author) / Bansal, Ajay (Thesis advisor) / Acuna, Ruben (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2019
Description
The functional programming paradigm is able to provide clean and concise solutions to many common programming problems, as well as promote safer, more testable code by encouraging an isolation of state-modifying behavior. Functional programming is finding its way into traditionally object-oriented and imperative languages, most notably with the introduction of Java 8 and in LINQ for C#. However, no functional programming language has achieved widespread adoption, meaning that students without a formal computer science background who learn technology on-demand for personal projects or for business may not come across functional programming in a significant way. Programmers need a reason to spend time learning these concepts to not miss out on the subtle but profound benefits they provide. I propose the use of a video game as an environment in which learning functional programming is the player's goal. In this carefully constructed video game, learning functional programming is the key to progression. Players will be motivated to learn and will be given an immediate chance to test and demonstrate their understanding. The game, named Lambda Starship (stylized as (lambda () starship)), is a 3D first-person video game. It takes place in a spaceship that, due to extreme magnetic interference, has lost all on-board software while leaving the hardware completely intact. The player is tasked to write software using functional programming paradigms to replace the old software and bring the spaceship back to a working state. Throughout the process, the player is guided by an in-game manual and other descriptive resources. The game is implemented in Unity and scripted using C#. The game's educational and entertainment value was evaluated with a study case. 24 undergraduate students at Arizona State University (ASU) played the game and were surveyed detailing their experience. During play, user statistics were recorded automatically, providing a data-driven way to analyze where players struggled with the concepts introduced in the game. Reception was neutral or positive in both the entertainment and educational sides of the game. A few players expressed concerns about the manual in its form factor and engagement value.
ContributorsCompton, Tyler Alexander (Author) / Gonzalez-Sanchez, Javier (Thesis director) / Bansal, Srividya (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
With the new independence of adulthood, college students are a group susceptible to adopting unsupported, if not harmful, health practices. A survey of Arizona State University undergraduate students (N=200) was conducted to evaluate supplement use, trust in information sources, and beliefs about supplement regulation. Of those who reported using supplements, college students most frequently received information from friends and family. STEM majors in fields unrelated to health who were taking a supplement were found to be less likely to receive information about the supplement from a medical practitioner than those in health fields or those in non-STEM majors (-26.9%, p=0.018). STEM majors in health-related fields were 15.0% more likely to treat colds and/or cold symptoms with research-supported methods identified from reliable sources, while non-health STEM and non-STEM majors were more likely to take unsupported cold treatments (p=0.010). Surveyed students, regardless of major, also stated they would trust a medical practitioner for supplement advice above other sources (88.0%), and the majority expressed a belief that dietary supplements are approved/regulated by the government (59.8%).
ContributorsPerez, Jacob Tanner (Author) / Hendrickson, Kirstin (Thesis director) / Lefler, Scott (Committee member) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Department of Physics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
ContributorsChandler, N. Kayla (Author) / Neisewander, Janet (Thesis director) / Sanabria, Federico (Committee member) / Olive, M. Foster (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2013-05
Description
I propose that norms regulate behaviors that negatively impact an individual's survival and reproduction. But because monitoring and enforcing of norms can be costly, individuals should be selective about which norms they police and under what circumstances they should do so. Two studies tested this idea by experimentally activating fitness-relevant motives and having participants answer questions about the policing of norms. The first study examined a norm prescribing respect for status and another proscribing sexual coercion. Results from Study 1 failed to support the hypotheses; activating a status-seeking motive did not have the predicted effects on policing of the respect-status norm nor did activating a mating motive have the predicted effects on policing of the respect-status norm or anti-coercion norm. Study 2 examined two new norms, one prescribing that people stay home when sick and the other proscribing people from having sex with another person's partners. Study 2 also manipulated whether self or others were the target of the policing. Study 2 failed to provide support; a disease avoidance motive failed to have effects on policing of the stay home when sick norm. Individuals in a relationship under a mating motive wanted less policing of others for violation of the mate poaching norm than those in a baseline condition, opposite of the predicted effects.
ContributorsSmith, M. Kristopher (Author) / Neuberg, L. Steven (Thesis director) / Presson, Clark (Committee member) / Hruschka, J. Daniel (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2013-05
Description
Literature in public administration emphasizes a growing dissatisfaction with government on the part of residents. Where there tends to be a lack in the literature is in terms of solutions to this problem. We would like to argue that the engagement process itself has the power to foster a profound attitudinal shift on the part of both residents and government. This paper explores the structural and cultural barriers to satisfactory public engagement both from literature and a combination of policy analysis, semi-structured interviews and participatory observation within the City of Tempe. We then provide recommendations to the City of Tempe on how to overcome these barriers and effect authentic public engagement practices. With these new suggested practices and mindsets, we provide a way that people can have the power to create their own community.
ContributorsRiffle, Morgan (Co-author) / Tchida, Celina (Co-author) / Ingram-Waters, Mary (Thesis director) / Grzanka, Patrick (Committee member) / King, Cheryl (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2013-05
Description
This thesis examines the relationship between unofficial, official, and parallel Islam in Uzbekistan following the end of the Soviet Union. Key touchstone moments in Uzbekistan during the twentieth-century show the history between unofficial and official Islam and the resulting precedents set for Muslims gathering against the government. This historical analysis shows how President Karimov and the Uzbek government view and approach Islam in the country following independence.
ContributorsTieslink, Evan (Author) / Batalden, Stephen (Thesis director) / Kefeli, Agnes (Committee member) / Saikia, Yasmin (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Politics and Global Studies (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2013-05
Description
Through this creative project, I executed a Distracted Driving Awareness Campaign at Arizona State University to raise awareness about the dangers of distracted driving, specifically texting while driving. As an Undergraduate Student Government Senator, my priority is the safety and success of students, both in and out of the classroom. By partnering with State Farm and AT&T, we were able to raise awareness about the dangers of distracted driving and collected over 200 pledges from students to never text and drive.
ContributorsHibbs, Jordan Ashley (Author) / Miller, Clark (Thesis director) / Parmentier, Mary Jane (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Department of Psychology (Contributor) / Graduate College (Contributor)
Created2014-05
Description
Brains and computers have been interacting since the invention of the computer. These two entities have worked together to accomplish a monumental set of goals, from landing man on the moon to helping to understand how the universe works on the most microscopic levels, and everything in between. As the years have gone on, the extent and depth of interaction between brains and computers have consistently widened, to the point where computers help brains with their thinking in virtually infinite everyday situations around the world. The first purpose of this research project was to conduct a brief review for the purposes of gaining a sound understanding of how both brains and computers operate at fundamental levels, and what it is about these two entities that allow them to work evermore seamlessly as the years go on. Next, a history of interaction between brains and computers was developed, which expanded upon the first task and helped to contribute to visions of future brain-computer interaction (BCI). The subsequent and primary task of this research project was to develop a theoretical framework for a potential brain-aiding device of the future. This was done by conducting an extensive literature review regarding the most advanced BCI technology in modern times and expanding upon the findings to argue feasibility of the future device and its components. Next, social predictions regarding the acceptance and use of the new technology were made by designing and executing a survey based on the Unified Theory of the Acceptance and Use of Technology (UTAUT). Finally, general economic predictions were inferred by examining several relationships between money and computers over time.
ContributorsThum, Giuseppe Edwardo (Author) / Gaffar, Ashraf (Thesis director) / Gonzalez-Sanchez, Javier (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Teen dating violence is a significant problem in the U.S., with approximately 1 out of 3 teens experiencing some form of dating violence. BLOOM is a not-for-profit organization created by Donna Bartos. BLOOM's educators enter high schools in Arizona and present their educational program on dating abuse prevention. BLOOM's primary goal is to educate teens on how to prevent teen dating violence and empower them with the skills leading to healthy relationships. After participants complete their educational program, a feedback card is filled out with an open-response section. This project focused on the open response section to analyze feedback cards through a process of code development, coding, and tallying. Information provided by this project could assist BLOOM in re-evaluating their curriculum, appealing to future investors, and growing their program to reach more students. With a coding system in place, BLOOM will also be able to better assess the impact they have on the participants of their program.
ContributorsHarmon, Ashley Nicole (Author) / Bodman, Denise (Thesis director) / Dumka, Larry (Committee member) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Liberal Arts and Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05